\name{eigenFrom} \alias{eigenFrom} \title{ Identify the Data Type to Obtain the Eigenvalues} \description{ The \code{eigenFrom} function identifies the data type from which to obtain the eigenvalues. The function is used internally in many functions of the \pkg{nFactors} package to be able to apply these to a vector of eigenvalues, a matrix of correlations or covariance or a \code{data.frame}. } \usage{ eigenFrom(x) } \arguments{ \item{x}{ numeric: a \code{vector} of eigenvalues, a \code{matrix} of correlations or of covariances or a \code{data.frame} of data} } \value{ \item{value}{ character: return the data type to obtain the eigenvalues: \code{"eigenvalues"}, \code{"correlation"} or \code{"data"} } } \author{ Gilles Raiche \cr Centre sur les Applications des Modeles de Reponses aux Items (CAMRI) \cr Universite du Quebec a Montreal\cr \email{raiche.gilles@uqam.ca}, \url{http://www.er.uqam.ca/nobel/r17165/} } \examples{ # ....................................................... # Different data types # Examples of adequate data sources # Vector of eigenvalues data(dFactors) x1 <- dFactors$Cliff1$eigenvalues eigenFrom(x1) # Data from a data.frame x2 <- data.frame(matrix(20*rnorm(100), ncol=5)) eigenFrom(x2) # From a covariance matrix x3 <- cov(x2) eigenFrom(x3) # From a correlation matrix x4 <- cor(x2) eigenFrom(x4) # Examples of inadequate data sources: not run because of errors generated # x0 <- c(2,1) # Error: not enough eigenvalues # eigenFrom(x0) # x2 <- matrix(x1, ncol=5) # Error: non a symetric covariance matrix # eigenFrom(x2) # eigenFrom(x3[,(1:2)]) # Error: not enough variables # x6 <- table(x5) # Error: not a valid data class # eigenFrom(x6) # ....................................................... } \keyword{ multivariate }